Felipe Giuste
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
-
- Artificial Intelligence in Healthcare
Papers in
-
- COVID-19 diagnosis using AI 9
- Co-authors
- May D. Wang (25 shared papers)Wenqi Shi (18 shared papers)Yuanda Zhu (12 shared papers)Monica Isgut (6 shared papers)Tong Li (5 shared papers)Anand Swaroop (2 shared papers)Vijender Chaitankar (2 shared papers)Gökhan Karakülah (2 shared papers)
- Journals
- Journal of Clinical Oncology (4 papers)Scientific Reports (3 papers)IEEE Reviews in Biomedical Engineering (3 papers)Blood (1 paper)IEEE Open Journal of Engineering in Medicine and Biology (1 paper)
- Partner nations
- United StatesSouth KoreaJapan
In The Last Decade
Felipe Giuste
34 papers receiving 509 citations
Peers
Comparison fields: 5 of 97
- Health Informatics 72
- Health Information Management 30
- Radiology, Nuclear Medicine and Imaging 82
- Molecular Biology 225
- Artificial Intelligence 94
Countries citing papers authored by Felipe Giuste
This map shows the geographic impact of Felipe Giuste's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Felipe Giuste with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Felipe Giuste more than expected).
Fields of papers citing papers by Felipe Giuste
This network shows the impact of papers produced by Felipe Giuste. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Felipe Giuste. The network helps show where Felipe Giuste may publish in the future.
Co-authors
The 25 scholars most cited alongside Felipe Giuste, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 91 | |
| 2 | 2022 | 84 | |
| 3 | 2016 | 55 | |
| 4 | 2023 | 45 | |
| 5 | 2012 | 41 | |
| 6 | 2018 | 28 | |
| 7 | 2020 | 25 | |
| 8 | 2021 | 14 | |
| 9 | 2023 | 12 | |
| 10 | 2022 | 11 | |
| 11 | 2023 | 10 | |
| 12 | 2021 | 10 | |
| 13 | 2022 | 10 | |
| 14 | 2022 | 8 | |
| 15 | 2023 | 7 | |
| 16 | 2022 | 7 | |
| 17 | 2021 | 6 | |
| 18 | 2022 | 6 | |
| 19 | 2021 | 6 | |
| 20 | 2025 | 5 |
About Felipe Giuste
Felipe Giuste is a scholar working on Radiology, Nuclear Medicine and Imaging, Molecular Biology, Artificial Intelligence, Surgery and Health Informatics, having authored 36 papers that have together received 519 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (9 papers), Artificial Intelligence in Healthcare and Education (6 papers), Machine Learning in Healthcare (6 papers), Transplantation: Methods and Outcomes (4 papers), Cancer Immunotherapy and Biomarkers (4 papers), Acute Ischemic Stroke Management (3 papers), Explainable Artificial Intelligence (XAI) (3 papers) and Scoliosis diagnosis and treatment (2 papers). The work is most often cited by research in Health Informatics (72 citations), Health Information Management (30 citations), Radiology, Nuclear Medicine and Imaging (82 citations), Molecular Biology (225 citations) and Artificial Intelligence (94 citations). Felipe Giuste has collaborated with scholars based in United States, South Korea and Japan. Frequent co-authors include May D. Wang, Wenqi Shi, Yuanda Zhu, Monica Isgut, Tong Li, Anand Swaroop, Vijender Chaitankar, Gökhan Karakülah, Matthew J. Brooks and Ying Sha. Their work appears in journals such as Journal of Clinical Oncology, Scientific Reports, IEEE Reviews in Biomedical Engineering, Blood and IEEE Open Journal of Engineering in Medicine and Biology.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.